Biologically Inspired Modelling for the Control of Upper Limb Movements: From Concept Studies to Future Applications

Modelling is continuously being deployed to gain knowledge on the mechanisms of motor control. Computational models, simulating the behaviour of complex systems, have often been used in combination with soft computing strategies, thus shifting the rationale of modelling from the description of a behaviour to the understanding of the mechanisms behind it. In this context, computational models are preferred to deterministic schemes because they deal better with complex systems. The literature offers some striking examples of biologically inspired modelling, which perform better than traditional approaches when dealing with both learning and adaptivity mechanisms. Can these theoretical studies be transferred into an application framework? That is, can biologically inspired models be used to implement rehabilitative devices? Some evidences, even if preliminary, are presented here, and support an affirmative answer to the previous question, thus opening new perspectives

Generation of Paths in a Maze using a Deep Network without Learning

Trajectory- or path-planning is a fundamental issue in a wide variety of applications. Here we show that it is possible to solve path planning for multiple start- and end-points highly efficiently with a network that consists only of max pooling layers, for which no network training is needed. Different from competing approaches, very large mazes containing more than half a billion nodes with dense obstacle configuration and several thousand path end-points can this way be solved in very short time on parallel hardware

Scale-Free Navigational Planning by Neuronal Traveling Waves

Spatial navigation and planning is assumed to involve a cognitive map for evaluating trajectories towards a goal. How such a map is realized in neuronal terms, however, remains elusive. Here we describe a simple and noise-robust neuronal implementation of a path finding algorithm in complex environments. We consider a neuronal map of the environment that supports a traveling wave spreading out from the goal location opposite to direction of the physical movement. At each position of the map, the smallest firing phase between adjacent neurons indicate the shortest direction towards the goal. In contrast to diffusion or single-wave-fronts, local phase differences build up in time at arbitrary distances from the goal, providing a minimal and robust directional information throughout the map. The time needed to reach the steady state represents an estimate of an agent's waiting time before it heads off to the goal. Given typical waiting times we estimate the minimal number of neurons involved in the cognitive map. In the context of the planning model, forward and backward spread of neuronal activity, oscillatory waves, and phase precession get a functional interpretation, allowing for speculations about the biological counterpart

A biologically inspired neural net for trajectory formation and obstacle avoidance

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A Biologically Inspired Neural Net for Trajectory Formation and Obstacle Avoidance.

A biologically inspired two-layered neural network for trajectory formation and obstacle avoidance is presented. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solutions (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time-evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result ..

Asservissement d’un système de navigation autonime par réseaux de neurones

Un système de navigation autonome ou robot mobile autonome doit être capable de percevoir son environnement, générer et exécuter une trajectoire en réaction appropriée à l’information perçue. Il faut pour cela le doter d'un système de navigation robuste. Ce mémoire présente une étude sur le contrôle du cap et de la vitesse d'un véhicule au moyen d'un perceptron multicouche entrainé avec l'algorithme de rétro-propagation du gradient. L'objectif est d’évaluer la capacité de ce type de réseau de neurones à contrôler le cap et la vitesse que doit adopter un robot mobile en fonction de ses entrées perceptives pour éviter d’entrer en collision avec les obstacles présents dans son voisinage. Pour ce faire, une stratégie de perception et de planification du déplacement a été développée et sa capacité à contrôler un mobile autonome a été montrée. Cette stratégie utilise deux réseaux de neurones fonctionnant en parallèle, l'un pour la commande du cap et l'autre pour la commande de la vitesse exploitant les deux le même système perceptif. Les résultats de la simulation sur Matlab sont présentés et analysés au regard de ce qu'est l'apprentissage statistique et de ce qu'on peut en attendre dans le cadre de la navigation autonome. Cette approche a l'avantage d'être simple et d'indiquer avec précision les degrés de rotation et la vitesse nécessaire avec une vision à long terme empêchant les blocages ou divergences de parcours contrairement aux approches habituelles rencontrées dans la littérature